An Equivalent-Satellite Method Exploiting Spatial Distribution to Reduce Fault Modes for ARAIM

Hangtian Qi, Xiaowei Cui, Xiang Wang, Gang Liu, Mingquan Lu

Peer Reviewed

Abstract: ARAIM exploiting MHSS needs to monitor a large number of fault modes in the case of multiple constellations, which poses a severe challenge to the computing power of the receiver. Currently, the most potent methods to release the heavy burden adopt the idea of subset consolidation, that is, one fault mode multiple replaces certain fault modes. We propose a framework to summarize the existing subset consolidation ways. However, there is a common defect that performance of ARAIM become unstable compared with baseline, and existing research does not explain and control this phenomenon. This paper analyzes the two key factors, the weighted DOP after separation and the number of subsets, which affect the detection threshold and cause that performance sometimes better or worse. Inspired by the key factors, we devise design guidelines of subset consolidation and propose an equivalent-satellite method based on spatial division referring to the satellite selection algorithm. With the result of significantly reducing the number of subsets, this method tight the detection threshold by remaining the geometry after separation as much as possible. We chose three algorithms to compare subset reduction, availability, performance, and stability. Simulation shows that the number of subsets is down to 12%, and it is a better choice than existing methods.
Published in: Proceedings of the 2022 International Technical Meeting of The Institute of Navigation
January 25 - 27, 2022
Hyatt Regency Long Beach
Long Beach, California
Pages: 273 - 283
Cite this article: Qi, Hangtian, Cui, Xiaowei, Wang, Xiang, Liu, Gang, Lu, Mingquan, "An Equivalent-Satellite Method Exploiting Spatial Distribution to Reduce Fault Modes for ARAIM," Proceedings of the 2022 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2022, pp. 273-283. https://doi.org/10.33012/2022.18211
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